weather_df =
rnoaa::meteo_pull_monitors(c("USW00094728", "USC00519397", "USS0023B17S"),
var = c("PRCP", "TMIN", "TMAX"),
date_min = "2017-01-01",
date_max = "2017-12-31") %>%
mutate(
name = recode(id, USW00094728 = "CentralPark_NY",
USC00519397 = "Waikiki_HA",
USS0023B17S = "Waterhole_WA"),
tmin = tmin / 10,
tmax = tmax / 10) %>%
select(name, id, everything())
## Registered S3 method overwritten by 'crul':
## method from
## as.character.form_file httr
## Registered S3 method overwritten by 'hoardr':
## method from
## print.cache_info httr
## file path: /Users/ariellecoq/Library/Caches/rnoaa/ghcnd/USW00094728.dly
## file last updated: 2019-09-26 10:25:30
## file min/max dates: 1869-01-01 / 2019-09-30
## file path: /Users/ariellecoq/Library/Caches/rnoaa/ghcnd/USC00519397.dly
## file last updated: 2019-09-26 10:25:43
## file min/max dates: 1965-01-01 / 2019-09-30
## file path: /Users/ariellecoq/Library/Caches/rnoaa/ghcnd/USS0023B17S.dly
## file last updated: 2019-09-26 10:25:48
## file min/max dates: 1999-09-01 / 2019-09-30
weather_df
## # A tibble: 1,095 x 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 CentralPark_NY USW00094728 2017-01-01 0 8.9 4.4
## 2 CentralPark_NY USW00094728 2017-01-02 53 5 2.8
## 3 CentralPark_NY USW00094728 2017-01-03 147 6.1 3.9
## 4 CentralPark_NY USW00094728 2017-01-04 0 11.1 1.1
## 5 CentralPark_NY USW00094728 2017-01-05 0 1.1 -2.7
## 6 CentralPark_NY USW00094728 2017-01-06 13 0.6 -3.8
## 7 CentralPark_NY USW00094728 2017-01-07 81 -3.2 -6.6
## 8 CentralPark_NY USW00094728 2017-01-08 0 -3.8 -8.8
## 9 CentralPark_NY USW00094728 2017-01-09 0 -4.9 -9.9
## 10 CentralPark_NY USW00094728 2017-01-10 0 7.8 -6
## # … with 1,085 more rows
Making new plots
weather_df %>%
ggplot (aes(x = tmin, y = tmax, color = name)) + geom_point(alpha = .5) +
labs(
title = "Temperature Plot",
x = "Minimum Daily Temperature",
y = "Maxiumum Daily Temperature",
caption = " Data from the rnoaa package"
)
## Warning: Removed 15 rows containing missing values (geom_point).

weather_df %>%
ggplot (aes(x = tmin, y = tmax, color = name)) + geom_point(alpha = .5) +
labs(
title = "Temperature Plot",
x = "Minimum Daily Temperature",
y = "Maxiumum Daily Temperature",
caption = " Data from the rnoaa package") +
scale_x_continuous(
breaks = c(-15, 0, 15),
labels = c("-15c", "0", "15"
))
## Warning: Removed 15 rows containing missing values (geom_point).

weather_df %>%
ggplot (aes(x = tmin, y = tmax, color = name)) + geom_point(alpha = .5) +
labs(
title = "Temperature Plot",
x = "Minimum Daily Temperature",
y = "Maxiumum Daily Temperature",
caption = " Data from the rnoaa package") +
scale_x_continuous(
breaks = c(-15, 0, 15),
labels = c("-15c", "0", "15"
)) +
scale_y_continuous(
trans = "sqrt"
)
## Warning in self$trans$transform(x): NaNs produced
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 90 rows containing missing values (geom_point).

Colors
weather_df %>%
ggplot (aes(x = tmin, y = tmax, color = name)) + geom_point(alpha = .5) +
labs(
title = "Temperature Plot",
x = "Minimum Daily Temperature",
y = "Maxiumum Daily Temperature",
caption = " Data from the rnoaa package"
) +
scale_color_hue (
name = "Weather Station",
h = c(50, 250)
)
## Warning: Removed 15 rows containing missing values (geom_point).

Viridis
ggp_base=
weather_df %>%
ggplot (aes(x = tmin, y = tmax, color = name)) + geom_point(alpha = .5) +
labs(
title = "Temperature Plot",
x = "Minimum Daily Temperature",
y = "Maxiumum Daily Temperature",
caption = " Data from the rnoaa package"
) +
viridis :: scale_color_viridis(
name = "Weather Station",
discrete = TRUE
)
ggp_base
## Warning: Removed 15 rows containing missing values (geom_point).

Themes
ggp_base +
theme(legend.position = "bottom")
## Warning: Removed 15 rows containing missing values (geom_point).

ggp_base +
theme_minimal() +
theme(legend.position = "bottom")
## Warning: Removed 15 rows containing missing values (geom_point).

More than one Dataset
central_park =
weather_df %>%
filter(name== "CentralPark_NY")
waikiki =
weather_df %>%
filter(name== "Waikiki_HA")
ggplot(data= waikiki, aes(x = date, y = tmax, color = name)) + geom_point()
## Warning: Removed 3 rows containing missing values (geom_point).

ggplot(data= waikiki, aes(x = date, y = tmax, color = name)) + geom_point()+
geom_line(data = central_park)
## Warning: Removed 3 rows containing missing values (geom_point).

ggplot(data= waikiki, aes(x = date, y = tmax, color = name)) +
geom_point(aes(size = prcp)) +
geom_line(data = central_park)
## Warning: Removed 3 rows containing missing values (geom_point).

PatchWork
ggp_scatter=
weather_df %>%
ggplot(aes(x= tmin, y = tmax)) +
geom_point()
ggp_density=
weather_df %>%
ggplot(aes(x= tmin)) +
geom_density()
ggp_box=
weather_df %>%
ggplot(aes(x= name, y = tmax)) +
geom_boxplot()
ggp_scatter + ggp_density
## Warning: Removed 15 rows containing missing values (geom_point).
## Warning: Removed 15 rows containing non-finite values (stat_density).

ggp_scatter + (ggp_density + ggp_box)
## Warning: Removed 15 rows containing missing values (geom_point).
## Warning: Removed 15 rows containing non-finite values (stat_density).
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

Data Maninpulation
weather_df %>%
mutate(
name = factor(name),
name = fct_relevel(name, "Waikiki_HA", "CentralPark_NY")
) %>%
ggplot(aes(x= name, y = tmax, color = name)) +
geom_boxplot()
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

weather_df %>%
mutate(
name = factor(name),
name = fct_reorder(name, tmax)
) %>%
ggplot(aes(x= name, y = tmax, color = name)) +
geom_boxplot()
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

restucture the plot
weather_df %>%
pivot_longer(
tmax:tmin,
names_to = "observation",
values_to = "temperature"
) %>%
ggplot (aes(x = temperature, fill = observation))+
geom_density (alpha = .5) +
facet_grid(~name) +
theme(legend.position = "bottom")
## Warning: Removed 18 rows containing non-finite values (stat_density).

pup_data =
read_csv("./data/FAS_pups.csv", col_types = "ciiiii") %>%
janitor::clean_names() %>%
mutate(sex = recode(sex, `1` = "male", `2` = "female"))
litter_data =
read_csv("./data/FAS_litters.csv", col_types = "ccddiiii") %>%
janitor::clean_names() %>%
select(-pups_survive) %>%
separate(group, into = c("dose", "day_of_tx"), sep = 3) %>%
mutate(wt_gain = gd18_weight - gd0_weight,
day_of_tx = as.numeric(day_of_tx))
fas_data = left_join(pup_data, litter_data, by = "litter_number")
fas_data %>%
pivot_longer(
pd_ears : pd_walk,
names_to = "outcome",
values_to = "pn_day"
) %>%
drop_na( dose, day_of_tx, pn_day) %>%
mutate(
outcome = factor(outcome),
outcome = fct_reorder (outcome, pn_day)
) %>%
ggplot (aes(x = dose, y = pn_day)) +
geom_violin() +
facet_grid(day_of_tx ~ outcome)
